Overview

Dataset statistics

Number of variables17
Number of observations13611
Missing cells0
Missing cells (%)0.0%
Duplicate rows68
Duplicate rows (%)0.5%
Total size in memory1.8 MiB
Average record size in memory136.0 B

Variable types

Numeric17

Alerts

Dataset has 68 (0.5%) duplicate rowsDuplicates
Area is highly overall correlated with Perimeter and 12 other fieldsHigh correlation
Perimeter is highly overall correlated with Area and 12 other fieldsHigh correlation
MajorAxisLength is highly overall correlated with Area and 13 other fieldsHigh correlation
MinorAxisLength is highly overall correlated with Area and 12 other fieldsHigh correlation
AspectRation is highly overall correlated with Area and 14 other fieldsHigh correlation
Eccentricity is highly overall correlated with Area and 13 other fieldsHigh correlation
ConvexArea is highly overall correlated with Area and 12 other fieldsHigh correlation
EquivDiameter is highly overall correlated with Area and 12 other fieldsHigh correlation
Solidity is highly overall correlated with roundness and 1 other fieldsHigh correlation
roundness is highly overall correlated with Area and 15 other fieldsHigh correlation
Compactness is highly overall correlated with Area and 14 other fieldsHigh correlation
ShapeFactor1 is highly overall correlated with Area and 12 other fieldsHigh correlation
ShapeFactor2 is highly overall correlated with Area and 13 other fieldsHigh correlation
ShapeFactor3 is highly overall correlated with Area and 14 other fieldsHigh correlation
ShapeFactor4 is highly overall correlated with MajorAxisLength and 6 other fieldsHigh correlation
Class is highly overall correlated with Area and 12 other fieldsHigh correlation
Extent is highly overall correlated with AspectRation and 4 other fieldsHigh correlation
Class has 2027 (14.9%) zerosZeros

Reproduction

Analysis started2022-11-23 16:07:38.164752
Analysis finished2022-11-23 16:10:07.653579
Duration2 minutes and 29.49 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Area
Real number (ℝ)

Distinct12011
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53048.285
Minimum20420
Maximum254616
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:07.978873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20420
5-th percentile27660.5
Q136328
median44652
Q361332
95-th percentile89824.5
Maximum254616
Range234196
Interquartile range (IQR)25004

Descriptive statistics

Standard deviation29324.096
Coefficient of variation (CV)0.55278122
Kurtosis10.800814
Mean53048.285
Median Absolute Deviation (MAD)10386
Skewness2.952931
Sum7.220402 × 108
Variance8.5990259 × 108
MonotonicityNot monotonic
2022-11-23T21:40:08.427978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35442 4
 
< 0.1%
38426 4
 
< 0.1%
34774 4
 
< 0.1%
40504 4
 
< 0.1%
52266 4
 
< 0.1%
34594 4
 
< 0.1%
33518 4
 
< 0.1%
28122 4
 
< 0.1%
38273 4
 
< 0.1%
36109 4
 
< 0.1%
Other values (12001) 13571
99.7%
ValueCountFrequency (%)
20420 1
< 0.1%
20464 1
< 0.1%
20548 1
< 0.1%
20711 1
< 0.1%
20786 1
< 0.1%
20942 1
< 0.1%
21101 1
< 0.1%
21314 1
< 0.1%
21348 1
< 0.1%
21397 1
< 0.1%
ValueCountFrequency (%)
254616 1
< 0.1%
251432 1
< 0.1%
248424 1
< 0.1%
241322 1
< 0.1%
237270 1
< 0.1%
234898 1
< 0.1%
233751 1
< 0.1%
231066 1
< 0.1%
230867 1
< 0.1%
226806 1
< 0.1%

Perimeter
Real number (ℝ)

Distinct13351
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean855.28346
Minimum524.736
Maximum1985.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:08.921233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum524.736
5-th percentile617.737
Q1703.5235
median794.941
Q3977.213
95-th percentile1181.124
Maximum1985.37
Range1460.634
Interquartile range (IQR)273.6895

Descriptive statistics

Standard deviation214.2897
Coefficient of variation (CV)0.25054816
Kurtosis3.5881233
Mean855.28346
Median Absolute Deviation (MAD)117.877
Skewness1.6261235
Sum11641263
Variance45920.074
MonotonicityNot monotonic
2022-11-23T21:40:09.362207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
701.644 3
 
< 0.1%
683.341 3
 
< 0.1%
911.589 3
 
< 0.1%
984.123 3
 
< 0.1%
805.168 3
 
< 0.1%
984.152 2
 
< 0.1%
740.017 2
 
< 0.1%
930.03 2
 
< 0.1%
639.039 2
 
< 0.1%
732.103 2
 
< 0.1%
Other values (13341) 13586
99.8%
ValueCountFrequency (%)
524.736 1
< 0.1%
524.932 1
< 0.1%
525.413 1
< 0.1%
528.408 1
< 0.1%
530.683 1
< 0.1%
530.825 1
< 0.1%
531.318 1
< 0.1%
533.701 1
< 0.1%
534.717 1
< 0.1%
534.918 1
< 0.1%
ValueCountFrequency (%)
1985.37 1
< 0.1%
1921.685 1
< 0.1%
1919.868 1
< 0.1%
1895.94 1
< 0.1%
1884.557 1
< 0.1%
1869.885 1
< 0.1%
1849.699 1
< 0.1%
1849.371 1
< 0.1%
1847.94 1
< 0.1%
1845.855 1
< 0.1%

MajorAxisLength
Real number (ℝ)

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean320.14187
Minimum183.60117
Maximum738.86015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:09.776817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum183.60117
5-th percentile224.64467
Q1253.30363
median296.88337
Q3376.49501
95-th percentile448.32394
Maximum738.86015
Range555.25899
Interquartile range (IQR)123.19138

Descriptive statistics

Standard deviation85.694186
Coefficient of variation (CV)0.26767566
Kurtosis2.5319021
Mean320.14187
Median Absolute Deviation (MAD)54.974243
Skewness1.3578153
Sum4357451
Variance7343.4935
MonotonicityNot monotonic
2022-11-23T21:40:10.184774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306.5338861 2
 
< 0.1%
365.7450534 2
 
< 0.1%
337.1714639 2
 
< 0.1%
347.4427551 2
 
< 0.1%
352.4820889 2
 
< 0.1%
365.2980956 2
 
< 0.1%
398.3767662 2
 
< 0.1%
404.3694213 2
 
< 0.1%
349.4594503 2
 
< 0.1%
396.8343705 2
 
< 0.1%
Other values (13533) 13591
99.9%
ValueCountFrequency (%)
183.601165 1
< 0.1%
183.9652515 1
< 0.1%
185.3819214 1
< 0.1%
186.0791494 1
< 0.1%
187.1686348 1
< 0.1%
188.9191604 1
< 0.1%
189.7993849 1
< 0.1%
190.2826321 1
< 0.1%
191.1765253 1
< 0.1%
191.2029134 1
< 0.1%
ValueCountFrequency (%)
738.8601535 1
< 0.1%
738.1445017 1
< 0.1%
726.3734932 1
< 0.1%
722.4940683 1
< 0.1%
721.2160984 1
< 0.1%
720.6955205 1
< 0.1%
719.1256904 1
< 0.1%
715.0530398 1
< 0.1%
713.9672821 1
< 0.1%
713.366837 1
< 0.1%

MinorAxisLength
Real number (ℝ)

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.27071
Minimum122.51265
Maximum460.1985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:10.614895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum122.51265
5-th percentile153.81363
Q1175.84817
median192.43173
Q3217.03174
95-th percentile267.88518
Maximum460.1985
Range337.68584
Interquartile range (IQR)41.183571

Descriptive statistics

Standard deviation44.970091
Coefficient of variation (CV)0.22232626
Kurtosis6.6510668
Mean202.27071
Median Absolute Deviation (MAD)19.063147
Skewness2.2382105
Sum2753106.7
Variance2022.3091
MonotonicityNot monotonic
2022-11-23T21:40:11.017251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160.5917844 2
 
< 0.1%
198.0564098 2
 
< 0.1%
160.0360674 2
 
< 0.1%
172.1287909 2
 
< 0.1%
189.3009514 2
 
< 0.1%
175.4863836 2
 
< 0.1%
182.0855177 2
 
< 0.1%
197.1457639 2
 
< 0.1%
202.9347314 2
 
< 0.1%
196.1712129 2
 
< 0.1%
Other values (13533) 13591
99.9%
ValueCountFrequency (%)
122.5126535 1
< 0.1%
129.5762069 1
< 0.1%
129.7481935 1
< 0.1%
130.7366895 1
< 0.1%
131.4330586 1
< 0.1%
132.1435531 1
< 0.1%
132.1875207 1
< 0.1%
132.298336 1
< 0.1%
132.3079693 1
< 0.1%
132.6402186 1
< 0.1%
ValueCountFrequency (%)
460.1984968 1
< 0.1%
450.9261867 1
< 0.1%
449.3396784 1
< 0.1%
447.4183287 1
< 0.1%
446.0436176 1
< 0.1%
440.686418 1
< 0.1%
435.8378565 1
< 0.1%
432.5894199 1
< 0.1%
432.3898213 1
< 0.1%
429.9621282 1
< 0.1%

AspectRation
Real number (ℝ)

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.583242
Minimum1.0248676
Maximum2.4303064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:11.450289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.0248676
5-th percentile1.208047
Q11.4323069
median1.5511237
Q31.7071089
95-th percentile2.0820115
Maximum2.4303064
Range1.4054389
Interquartile range (IQR)0.274802

Descriptive statistics

Standard deviation0.24667846
Coefficient of variation (CV)0.15580591
Kurtosis0.11381444
Mean1.583242
Median Absolute Deviation (MAD)0.13542285
Skewness0.5825734
Sum21549.507
Variance0.06085026
MonotonicityNot monotonic
2022-11-23T21:40:11.849107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.908776886 2
 
< 0.1%
1.846671127 2
 
< 0.1%
2.106846721 2
 
< 0.1%
2.018504594 2
 
< 0.1%
1.862019637 2
 
< 0.1%
2.081632137 2
 
< 0.1%
2.187855307 2
 
< 0.1%
2.051118996 2
 
< 0.1%
1.722028791 2
 
< 0.1%
2.022898083 2
 
< 0.1%
Other values (13533) 13591
99.9%
ValueCountFrequency (%)
1.024867596 1
< 0.1%
1.036422681 1
< 0.1%
1.041963699 1
< 0.1%
1.047159612 1
< 0.1%
1.049180121 1
< 0.1%
1.049424005 1
< 0.1%
1.060785687 1
< 0.1%
1.06079802 1
< 0.1%
1.064697761 1
< 0.1%
1.065247205 1
< 0.1%
ValueCountFrequency (%)
2.430306447 1
< 0.1%
2.388873436 1
< 0.1%
2.387394886 1
< 0.1%
2.383260481 1
< 0.1%
2.364016609 1
< 0.1%
2.358271756 1
< 0.1%
2.350473191 1
< 0.1%
2.347666241 1
< 0.1%
2.345077704 1
< 0.1%
2.34329324 1
< 0.1%

Eccentricity
Real number (ℝ)

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75089493
Minimum0.21895126
Maximum0.91142297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:12.282269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.21895126
5-th percentile0.56104933
Q10.71592775
median0.76444081
Q30.81046598
95-th percentile0.87710167
Maximum0.91142297
Range0.6924717
Interquartile range (IQR)0.094538238

Descriptive statistics

Standard deviation0.092001763
Coefficient of variation (CV)0.12252282
Kurtosis1.3874556
Mean0.75089493
Median Absolute Deviation (MAD)0.047162019
Skewness-1.0628239
Sum10220.431
Variance0.0084643244
MonotonicityNot monotonic
2022-11-23T21:40:12.592648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.851782424 2
 
< 0.1%
0.840691128 2
 
< 0.1%
0.880178416 2
 
< 0.1%
0.868655702 2
 
< 0.1%
0.843549607 2
 
< 0.1%
0.877053739 2
 
< 0.1%
0.889431436 2
 
< 0.1%
0.873101348 2
 
< 0.1%
0.81411022 2
 
< 0.1%
0.86926848 2
 
< 0.1%
Other values (13533) 13591
99.9%
ValueCountFrequency (%)
0.218951263 1
< 0.1%
0.262774375 1
< 0.1%
0.280936588 1
< 0.1%
0.296720861 1
< 0.1%
0.302576233 1
< 0.1%
0.303273094 1
< 0.1%
0.333648694 1
< 0.1%
0.333679658 1
< 0.1%
0.343278441 1
< 0.1%
0.344601032 1
< 0.1%
ValueCountFrequency (%)
0.911422968 1
< 0.1%
0.908167319 1
< 0.1%
0.908047776 1
< 0.1%
0.907712239 1
< 0.1%
0.90612554 1
< 0.1%
0.90564377 1
< 0.1%
0.904983696 1
< 0.1%
0.904744384 1
< 0.1%
0.904522875 1
< 0.1%
0.904369714 1
< 0.1%

ConvexArea
Real number (ℝ)

Distinct12066
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53768.2
Minimum20684
Maximum263261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:12.952690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20684
5-th percentile28011.5
Q136714.5
median45178
Q362294
95-th percentile91282
Maximum263261
Range242577
Interquartile range (IQR)25579.5

Descriptive statistics

Standard deviation29774.916
Coefficient of variation (CV)0.55376441
Kurtosis10.74364
Mean53768.2
Median Absolute Deviation (MAD)10566
Skewness2.9418211
Sum7.3183897 × 108
Variance8.8654561 × 108
MonotonicityNot monotonic
2022-11-23T21:40:13.379216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37023 5
 
< 0.1%
38941 4
 
< 0.1%
43198 4
 
< 0.1%
47888 4
 
< 0.1%
41706 4
 
< 0.1%
31423 4
 
< 0.1%
47446 4
 
< 0.1%
45243 4
 
< 0.1%
47729 4
 
< 0.1%
44262 4
 
< 0.1%
Other values (12056) 13570
99.7%
ValueCountFrequency (%)
20684 1
< 0.1%
20772 1
< 0.1%
20825 1
< 0.1%
20988 1
< 0.1%
21057 1
< 0.1%
21191 1
< 0.1%
21462 1
< 0.1%
21587 1
< 0.1%
21590 1
< 0.1%
21731 1
< 0.1%
ValueCountFrequency (%)
263261 1
< 0.1%
257425 1
< 0.1%
251082 1
< 0.1%
244319 1
< 0.1%
240671 1
< 0.1%
238265 1
< 0.1%
237344 1
< 0.1%
233243 1
< 0.1%
232903 1
< 0.1%
231500 1
< 0.1%

EquivDiameter
Real number (ℝ)

Distinct12011
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.06422
Minimum161.24376
Maximum569.37436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:14.057040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum161.24376
5-th percentile187.66577
Q1215.068
median238.43803
Q3279.44647
95-th percentile338.18354
Maximum569.37436
Range408.13059
Interquartile range (IQR)64.378463

Descriptive statistics

Standard deviation59.17712
Coefficient of variation (CV)0.2338423
Kurtosis5.1920573
Mean253.06422
Median Absolute Deviation (MAD)28.341218
Skewness1.9489576
Sum3444457.1
Variance3501.9315
MonotonicityNot monotonic
2022-11-23T21:40:14.640761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212.4291787 4
 
< 0.1%
221.1911001 4
 
< 0.1%
210.4177557 4
 
< 0.1%
227.0931406 4
 
< 0.1%
257.9673197 4
 
< 0.1%
209.8724584 4
 
< 0.1%
206.5827753 4
 
< 0.1%
189.2248464 4
 
< 0.1%
220.7503049 4
 
< 0.1%
214.4187649 4
 
< 0.1%
Other values (12001) 13571
99.7%
ValueCountFrequency (%)
161.2437642 1
< 0.1%
161.4173908 1
< 0.1%
161.7483421 1
< 0.1%
162.3886209 1
< 0.1%
162.6823813 1
< 0.1%
163.29171 1
< 0.1%
163.9104256 1
< 0.1%
164.7356296 1
< 0.1%
164.86697 1
< 0.1%
165.0560709 1
< 0.1%
ValueCountFrequency (%)
569.3743583 1
< 0.1%
565.8031152 1
< 0.1%
562.4084465 1
< 0.1%
554.3110259 1
< 0.1%
549.6376504 1
< 0.1%
546.883372 1
< 0.1%
545.5465304 1
< 0.1%
542.4042484 1
< 0.1%
542.1706318 1
< 0.1%
537.381027 1
< 0.1%

Extent
Real number (ℝ)

Distinct13535
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74973279
Minimum0.55531472
Maximum0.86619464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:15.214013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.55531472
5-th percentile0.6551083
Q10.71863353
median0.75985895
Q30.7868515
95-th percentile0.81225291
Maximum0.86619464
Range0.31087992
Interquartile range (IQR)0.068217969

Descriptive statistics

Standard deviation0.049086367
Coefficient of variation (CV)0.065471816
Kurtosis0.64331884
Mean0.74973279
Median Absolute Deviation (MAD)0.032074741
Skewness-0.89534843
Sum10204.613
Variance0.0024094714
MonotonicityNot monotonic
2022-11-23T21:40:15.638759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.708824975 2
 
< 0.1%
0.749505266 2
 
< 0.1%
0.819059011 2
 
< 0.1%
0.725920615 2
 
< 0.1%
0.757630139 2
 
< 0.1%
0.787569967 2
 
< 0.1%
0.601419526 2
 
< 0.1%
0.627271882 2
 
< 0.1%
0.787711843 2
 
< 0.1%
0.789365079 2
 
< 0.1%
Other values (13525) 13591
99.9%
ValueCountFrequency (%)
0.555314717 1
< 0.1%
0.566669254 1
< 0.1%
0.566767527 1
< 0.1%
0.57023762 1
< 0.1%
0.570274961 1
< 0.1%
0.572208854 1
< 0.1%
0.572379135 1
< 0.1%
0.573367347 1
< 0.1%
0.574040364 1
< 0.1%
0.574417687 1
< 0.1%
ValueCountFrequency (%)
0.866194641 1
< 0.1%
0.85841985 1
< 0.1%
0.852841426 1
< 0.1%
0.852268603 1
< 0.1%
0.851948052 1
< 0.1%
0.850744228 1
< 0.1%
0.850251311 1
< 0.1%
0.848622642 1
< 0.1%
0.84797499 1
< 0.1%
0.846362465 1
< 0.1%

Solidity
Real number (ℝ)

Distinct13522
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98714284
Minimum0.91924616
Maximum0.9946775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:16.104098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.91924616
5-th percentile0.97840498
Q10.9856704
median0.988283
Q30.99001311
95-th percentile0.99202732
Maximum0.9946775
Range0.075431343
Interquartile range (IQR)0.0043427115

Descriptive statistics

Standard deviation0.0046603792
Coefficient of variation (CV)0.0047210788
Kurtosis12.799621
Mean0.98714284
Median Absolute Deviation (MAD)0.002029079
Skewness-2.5500931
Sum13436.001
Variance2.1719134 × 10-5
MonotonicityNot monotonic
2022-11-23T21:40:16.501506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.982329069 2
 
< 0.1%
0.991076264 2
 
< 0.1%
0.990205184 2
 
< 0.1%
0.980017293 2
 
< 0.1%
0.986691276 2
 
< 0.1%
0.979472301 2
 
< 0.1%
0.986484175 2
 
< 0.1%
0.989206349 2
 
< 0.1%
0.991359178 2
 
< 0.1%
0.981596903 2
 
< 0.1%
Other values (13512) 13591
99.9%
ValueCountFrequency (%)
0.919246157 1
< 0.1%
0.943559301 1
< 0.1%
0.944568068 1
< 0.1%
0.946633681 1
< 0.1%
0.946719096 1
< 0.1%
0.947666764 1
< 0.1%
0.949023205 1
< 0.1%
0.949102529 1
< 0.1%
0.951947517 1
< 0.1%
0.953296108 1
< 0.1%
ValueCountFrequency (%)
0.9946775 1
< 0.1%
0.994378182 1
< 0.1%
0.994259633 1
< 0.1%
0.994213484 1
< 0.1%
0.99408218 1
< 0.1%
0.994081773 1
< 0.1%
0.994049175 1
< 0.1%
0.993945441 1
< 0.1%
0.993919333 1
< 0.1%
0.993905511 1
< 0.1%

roundness
Real number (ℝ)

Distinct13540
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87328183
Minimum0.48961826
Maximum0.9906854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:16.880987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.48961826
5-th percentile0.77095546
Q10.83209633
median0.88315729
Q30.91686883
95-th percentile0.95925361
Maximum0.9906854
Range0.50106714
Interquartile range (IQR)0.084772504

Descriptive statistics

Standard deviation0.059519888
Coefficient of variation (CV)0.068156563
Kurtosis0.37430633
Mean0.87328183
Median Absolute Deviation (MAD)0.039831678
Skewness-0.63574895
Sum11886.239
Variance0.0035426171
MonotonicityNot monotonic
2022-11-23T21:40:17.264171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.78228301 2
 
< 0.1%
0.798728556 2
 
< 0.1%
0.789488352 2
 
< 0.1%
0.788808932 2
 
< 0.1%
0.814603545 2
 
< 0.1%
0.822849045 2
 
< 0.1%
0.748511932 2
 
< 0.1%
0.787385321 2
 
< 0.1%
0.794389513 2
 
< 0.1%
0.813728734 2
 
< 0.1%
Other values (13530) 13591
99.9%
ValueCountFrequency (%)
0.489618256 1
< 0.1%
0.556765826 1
< 0.1%
0.57180091 1
< 0.1%
0.576028662 1
< 0.1%
0.577843594 1
< 0.1%
0.59370828 1
< 0.1%
0.595048401 1
< 0.1%
0.601978543 1
< 0.1%
0.605399396 1
< 0.1%
0.60656967 1
< 0.1%
ValueCountFrequency (%)
0.9906854 1
< 0.1%
0.987919735 1
< 0.1%
0.987089244 1
< 0.1%
0.986812285 1
< 0.1%
0.986684731 1
< 0.1%
0.986114961 1
< 0.1%
0.986102963 1
< 0.1%
0.985949497 1
< 0.1%
0.984965881 1
< 0.1%
0.984877069 1
< 0.1%

Compactness
Real number (ℝ)

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79986368
Minimum0.64057676
Maximum0.98730297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:17.647622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.64057676
5-th percentile0.69056671
Q10.76246875
median0.80127668
Q30.83426991
95-th percentile0.90898975
Maximum0.98730297
Range0.34672621
Interquartile range (IQR)0.071801164

Descriptive statistics

Standard deviation0.061713463
Coefficient of variation (CV)0.077154976
Kurtosis-0.22345947
Mean0.79986368
Median Absolute Deviation (MAD)0.035725658
Skewness0.037115458
Sum10886.945
Variance0.0038085515
MonotonicityNot monotonic
2022-11-23T21:40:18.027858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.721597149 2
 
< 0.1%
0.724485451 2
 
< 0.1%
0.685669954 2
 
< 0.1%
0.70305102 2
 
< 0.1%
0.732188358 2
 
< 0.1%
0.691954132 2
 
< 0.1%
0.674817096 2
 
< 0.1%
0.696376459 2
 
< 0.1%
0.75144569 2
 
< 0.1%
0.70109382 2
 
< 0.1%
Other values (13533) 13591
99.9%
ValueCountFrequency (%)
0.640576759 1
< 0.1%
0.645367658 1
< 0.1%
0.645796363 1
< 0.1%
0.646562019 1
< 0.1%
0.64876196 1
< 0.1%
0.649038704 1
< 0.1%
0.649189479 1
< 0.1%
0.651630803 1
< 0.1%
0.65187098 1
< 0.1%
0.652148018 1
< 0.1%
ValueCountFrequency (%)
0.987302969 1
< 0.1%
0.981611143 1
< 0.1%
0.979431992 1
< 0.1%
0.97673884 1
< 0.1%
0.97589476 1
< 0.1%
0.975594507 1
< 0.1%
0.970515523 1
< 0.1%
0.970471434 1
< 0.1%
0.968733066 1
< 0.1%
0.968443994 1
< 0.1%

ShapeFactor1
Real number (ℝ)

Distinct13521
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0065636085
Minimum0.002778013
Maximum0.010451169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:18.430083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.002778013
5-th percentile0.004781539
Q10.005899917
median0.006645174
Q30.00727142
95-th percentile0.008305289
Maximum0.010451169
Range0.007673156
Interquartile range (IQR)0.001371503

Descriptive statistics

Standard deviation0.0011279982
Coefficient of variation (CV)0.17185641
Kurtosis0.71435483
Mean0.0065636085
Median Absolute Deviation (MAD)0.000677032
Skewness-0.53414055
Sum89.337275
Variance1.27238 × 10-6
MonotonicityNot monotonic
2022-11-23T21:40:18.791731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.006754749 2
 
< 0.1%
0.006310487 2
 
< 0.1%
0.00706211 2
 
< 0.1%
0.007078945 2
 
< 0.1%
0.007355746 2
 
< 0.1%
0.006492974 2
 
< 0.1%
0.006321996 2
 
< 0.1%
0.007356237 2
 
< 0.1%
0.006908984 2
 
< 0.1%
0.006506303 2
 
< 0.1%
Other values (13511) 13591
99.9%
ValueCountFrequency (%)
0.002778013 1
< 0.1%
0.002855716 1
< 0.1%
0.00286012 1
< 0.1%
0.002878357 1
< 0.1%
0.002901861 1
< 0.1%
0.002909101 1
< 0.1%
0.002942455 1
< 0.1%
0.002959317 1
< 0.1%
0.002974423 1
< 0.1%
0.002984943 1
< 0.1%
ValueCountFrequency (%)
0.010451169 1
< 0.1%
0.009897003 1
< 0.1%
0.00982398 1
< 0.1%
0.009747874 1
< 0.1%
0.009720056 1
< 0.1%
0.009669149 1
< 0.1%
0.00966647 1
< 0.1%
0.009661587 1
< 0.1%
0.009652959 1
< 0.1%
0.009612311 1
< 0.1%

ShapeFactor2
Real number (ℝ)

Distinct13506
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0017159473
Minimum0.000564169
Maximum0.003664972
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:19.181649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.000564169
5-th percentile0.0009112715
Q10.00115352
median0.001693531
Q30.002170268
95-th percentile0.002724234
Maximum0.003664972
Range0.003100803
Interquartile range (IQR)0.001016748

Descriptive statistics

Standard deviation0.00059587485
Coefficient of variation (CV)0.34725707
Kurtosis-0.8592542
Mean0.0017159473
Median Absolute Deviation (MAD)0.000512899
Skewness0.30122592
Sum23.355759
Variance3.5506684 × 10-7
MonotonicityNot monotonic
2022-11-23T21:40:19.491608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00109864 3
 
< 0.1%
0.001015771 2
 
< 0.1%
0.002388518 2
 
< 0.1%
0.000935566 2
 
< 0.1%
0.001043913 2
 
< 0.1%
0.00091597 2
 
< 0.1%
0.001209774 2
 
< 0.1%
0.00105393 2
 
< 0.1%
0.001046791 2
 
< 0.1%
0.000979686 2
 
< 0.1%
Other values (13496) 13590
99.8%
ValueCountFrequency (%)
0.000564169 1
< 0.1%
0.000566989 1
< 0.1%
0.000567843 1
< 0.1%
0.000572451 1
< 0.1%
0.000589955 1
< 0.1%
0.000590595 1
< 0.1%
0.000592256 1
< 0.1%
0.000601694 1
< 0.1%
0.000605579 1
< 0.1%
0.000606765 1
< 0.1%
ValueCountFrequency (%)
0.003664972 1
< 0.1%
0.003573241 1
< 0.1%
0.003563624 1
< 0.1%
0.003545182 1
< 0.1%
0.003506417 1
< 0.1%
0.003470231 1
< 0.1%
0.003434872 1
< 0.1%
0.003418781 1
< 0.1%
0.003412363 1
< 0.1%
0.003392637 1
< 0.1%

ShapeFactor3
Real number (ℝ)

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64359018
Minimum0.41033858
Maximum0.97476715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:20.621388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.41033858
5-th percentile0.47688238
Q10.58135859
median0.64204432
Q30.69600628
95-th percentile0.82626237
Maximum0.97476715
Range0.56442857
Interquartile range (IQR)0.11464769

Descriptive statistics

Standard deviation0.09899615
Coefficient of variation (CV)0.15381862
Kurtosis-0.14447504
Mean0.64359018
Median Absolute Deviation (MAD)0.057161848
Skewness0.24248093
Sum8759.906
Variance0.0098002378
MonotonicityNot monotonic
2022-11-23T21:40:20.983533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.520702445 2
 
< 0.1%
0.524879169 2
 
< 0.1%
0.470143286 2
 
< 0.1%
0.494280737 2
 
< 0.1%
0.536099791 2
 
< 0.1%
0.478800521 2
 
< 0.1%
0.455378113 2
 
< 0.1%
0.484940173 2
 
< 0.1%
0.564670625 2
 
< 0.1%
0.491532544 2
 
< 0.1%
Other values (13533) 13591
99.9%
ValueCountFrequency (%)
0.410338584 1
< 0.1%
0.416499415 1
< 0.1%
0.417052942 1
< 0.1%
0.418042444 1
< 0.1%
0.420892081 1
< 0.1%
0.421251239 1
< 0.1%
0.42144698 1
< 0.1%
0.424622703 1
< 0.1%
0.424935775 1
< 0.1%
0.425297037 1
< 0.1%
ValueCountFrequency (%)
0.974767153 1
< 0.1%
0.963560435 1
< 0.1%
0.959287027 1
< 0.1%
0.954018762 1
< 0.1%
0.952370582 1
< 0.1%
0.951784642 1
< 0.1%
0.941900381 1
< 0.1%
0.941814803 1
< 0.1%
0.938443753 1
< 0.1%
0.937883769 1
< 0.1%

ShapeFactor4
Real number (ℝ)

Distinct13532
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99506331
Minimum0.9476874
Maximum0.99973253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:21.298917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.9476874
5-th percentile0.9869831
Q10.9937029
median0.99638594
Q30.99788254
95-th percentile0.99899198
Maximum0.99973253
Range0.052045127
Interquartile range (IQR)0.0041796475

Descriptive statistics

Standard deviation0.0043664577
Coefficient of variation (CV)0.0043881205
Kurtosis13.038067
Mean0.99506331
Median Absolute Deviation (MAD)0.001817961
Skewness-2.7594829
Sum13543.807
Variance1.9065953 × 10-5
MonotonicityNot monotonic
2022-11-23T21:40:21.701484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.991327833 2
 
< 0.1%
0.969279207 2
 
< 0.1%
0.997805139 2
 
< 0.1%
0.993904791 2
 
< 0.1%
0.972379074 2
 
< 0.1%
0.996686551 2
 
< 0.1%
0.990519842 2
 
< 0.1%
0.996467418 2
 
< 0.1%
0.996301421 2
 
< 0.1%
0.998228338 2
 
< 0.1%
Other values (13522) 13591
99.9%
ValueCountFrequency (%)
0.947687403 1
< 0.1%
0.949990311 1
< 0.1%
0.951238836 1
< 0.1%
0.95503249 1
< 0.1%
0.956866857 1
< 0.1%
0.957232663 1
< 0.1%
0.957325108 1
< 0.1%
0.957327553 1
< 0.1%
0.957747709 1
< 0.1%
0.958284825 1
< 0.1%
ValueCountFrequency (%)
0.99973253 1
< 0.1%
0.999709101 1
< 0.1%
0.999707336 1
< 0.1%
0.999680521 1
< 0.1%
0.999674292 1
< 0.1%
0.999670452 1
< 0.1%
0.999659526 1
< 0.1%
0.999652276 1
< 0.1%
0.999637011 1
< 0.1%
0.999625379 1
< 0.1%

Class
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6311807
Minimum0
Maximum6
Zeros2027
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-11-23T21:40:22.013800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1517815
Coefficient of variation (CV)0.59258453
Kurtosis-1.1468314
Mean3.6311807
Median Absolute Deviation (MAD)2
Skewness-0.51495129
Sum49424
Variance4.6301636
MonotonicityIncreasing
2022-11-23T21:40:22.233964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 3546
26.1%
5 2636
19.4%
0 2027
14.9%
4 1928
14.2%
3 1630
12.0%
1 1322
 
9.7%
2 522
 
3.8%
ValueCountFrequency (%)
0 2027
14.9%
1 1322
 
9.7%
2 522
 
3.8%
3 1630
12.0%
4 1928
14.2%
5 2636
19.4%
6 3546
26.1%
ValueCountFrequency (%)
6 3546
26.1%
5 2636
19.4%
4 1928
14.2%
3 1630
12.0%
2 522
 
3.8%
1 1322
 
9.7%
0 2027
14.9%

Interactions

2022-11-23T21:39:58.537608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:37:59.637607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:05.264252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:13.306249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:19.594118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:26.803432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:36.490934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:43.394475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:51.237456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:58.204960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:05.479836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:13.610389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:20.585889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:27.958171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:33.938158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:40.785007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:50.258450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:58.888040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:37:59.921769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:05.670151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:13.756401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:19.892294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-11-23T21:40:04.121044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-11-23T21:38:11.423691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:18.252937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:24.699092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:34.810169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:41.525376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:49.311313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:56.310073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:03.694186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:11.933236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:19.120820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:26.129021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:32.872358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:39.132564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:48.631602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:56.706111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:40:04.503041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:04.070918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:11.890157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:18.635901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:25.249155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:35.186074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:41.997740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:49.791728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:56.645464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:04.128265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:12.363273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:19.469560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:26.578567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:33.203285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:39.539501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:49.092792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:57.147780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:40:04.876714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:04.448166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:12.345582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:18.989467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:25.796459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:35.578863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:42.478959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:50.284275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:56.980515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:04.556807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:12.765135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:19.824386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:27.040711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:33.423837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:39.946182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:49.486429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:57.638528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:40:05.252585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:04.849014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:12.827723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:19.300394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:26.305995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:36.023468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:42.929398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:50.777508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:38:57.739727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:04.993770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:13.144088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:20.211051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:27.490863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:33.684417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:40.331755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:49.853067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-11-23T21:39:58.129408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-11-23T21:40:22.528029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-23T21:40:23.146204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-23T21:40:23.723433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-23T21:40:24.321665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-23T21:40:24.945096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-23T21:40:05.928413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-23T21:40:07.164244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AreaPerimeterMajorAxisLengthMinorAxisLengthAspectRationEccentricityConvexAreaEquivDiameterExtentSolidityroundnessCompactnessShapeFactor1ShapeFactor2ShapeFactor3ShapeFactor4Class
028395610.291208.178117173.8887471.1971910.54981228715190.1410970.7639230.9888560.9580270.9133580.0073320.0031470.8342220.9987240
128734638.018200.524796182.7344191.0973560.41178529172191.2727510.7839680.9849860.8870340.9538610.0069790.0035640.9098510.9984300
229380624.110212.826130175.9311431.2097130.56272729690193.4109040.7781130.9895590.9478490.9087740.0072440.0030480.8258710.9990660
330008645.884210.557999182.5165161.1536380.49861630724195.4670620.7826810.9766960.9039360.9283290.0070170.0032150.8617940.9941990
430140620.134201.847882190.2792791.0607980.33368030417195.8965030.7730980.9908930.9848770.9705160.0066970.0036650.9419000.9991660
530279634.927212.560556181.5101821.1710670.52040130600196.3477020.7756880.9895100.9438520.9237260.0070200.0031530.8532700.9992360
630477670.033211.050155184.0390501.1467680.48947830970196.9886330.7624020.9840810.8530800.9333740.0069250.0032420.8711860.9990490
730519629.727212.996755182.7372041.1655910.51376030847197.1243200.7706820.9893670.9671090.9254800.0069790.0031580.8565140.9983450
830685635.681213.534145183.1571461.1658520.51408131044197.6596960.7715610.9884360.9542400.9256580.0069590.0031520.8568440.9989530
930834631.934217.227813180.8974691.2008340.55364231120198.1390120.7836830.9908100.9702780.9121250.0070450.0030080.8319730.9990610
AreaPerimeterMajorAxisLengthMinorAxisLengthAspectRationEccentricityConvexAreaEquivDiameterExtentSolidityroundnessCompactnessShapeFactor1ShapeFactor2ShapeFactor3ShapeFactor4Class
1360142042771.515288.082674186.3470901.5459470.76261542476231.3645110.8162540.9897820.8875740.8031180.0068520.0017580.6449990.9971346
1360242047768.936292.975007183.1391411.5997400.78054342446231.3782680.7738900.9906000.8936440.7897540.0069680.0016720.6237120.9977776
1360342049770.185290.163403185.0516851.5680130.77024342503231.3837710.7560050.9893180.8907900.7974260.0069010.0017210.6358880.9970806
1360442070763.489289.022373186.1234341.5528530.76504642556231.4415430.7688230.9885800.9069360.8007740.0068700.0017430.6412390.9957506
1360542070760.701276.691651193.9453671.4266470.71321642458231.4415430.7308130.9908620.9135960.8364600.0065770.0019860.6996660.9981766
1360642097759.696288.721612185.9447051.5527280.76500242508231.5157990.7145740.9903310.9166030.8018650.0068580.0017490.6429880.9983856
1360742101757.499281.576392190.7131361.4764390.73570242494231.5267980.7999430.9907520.9220150.8222520.0066880.0018860.6760990.9982196
1360842139759.321281.539928191.1879791.4725820.73406542569231.6312610.7299320.9898990.9184240.8227300.0066810.0018880.6768840.9967676
1360942147763.779283.382636190.2757311.4893260.74105542667231.6532470.7053890.9878130.9079060.8174570.0067240.0018520.6682370.9952226
1361042159772.237295.142741182.2047161.6198410.78669342600231.6862230.7889620.9896480.8883800.7849970.0070010.0016400.6162210.9981806

Duplicate rows

Most frequently occurring

AreaPerimeterMajorAxisLengthMinorAxisLengthAspectRationEccentricityConvexAreaEquivDiameterExtentSolidityroundnessCompactnessShapeFactor1ShapeFactor2ShapeFactor3ShapeFactor4Class# duplicates
033518702.956277.571399154.3055811.7988420.83124034023206.5827750.8083830.9851570.8523770.7442510.0082810.0015670.5539090.99639642
133954716.750277.368480156.3563261.7739510.82597034420207.9220420.7994820.9864610.8305490.7496240.0081690.0015910.5619360.99684742
238427756.323306.533886160.5917841.9087770.85178238773221.1939780.7969760.9910760.8441740.7215970.0079770.0013340.5207020.99390542
338891791.343319.499996156.8696192.0367230.87116839651222.5254120.6500250.9808330.7804220.6964800.0082150.0011920.4850850.98798342
440804790.802323.475648163.2877171.9810160.86324141636227.9325920.7875700.9800170.8199310.7046360.0079280.0012060.4965120.98359842
541978821.864337.171464160.0360672.1068470.88017842593231.1883420.6848850.9855610.7809650.6856700.0080320.0010950.4701430.99052042
642156815.245335.198243160.9369382.0827920.87720042586231.6779800.8340460.9899030.7970640.6911670.0079510.0011190.4777120.99497542
742450828.116347.951525156.4693662.2237680.89318642820232.4844480.6093880.9913590.7778670.6681520.0081970.0010080.4464270.99275042
843099815.390328.234078168.6101161.9467050.85797743710234.2548850.6976660.9860220.8146040.7136820.0076160.0012190.5093430.99153942
943746836.693339.352567165.4114422.0515660.87316144442236.0066460.7137780.9843390.7852640.6954620.0077570.0011190.4836670.99227442